Service quality measurements for IPv6 inter-networks

Measurement-based performance evaluation of network traffic is becoming very important, especially for networks trying to provide differentiated levels of service quality to the different application flows. The non-identical response of flows to the different types of network-imposed performance degradation raises the need for ubiquitous measurement mechanisms, able to measure numerous performance properties, and being equally applicable to different applications and transports. This paper presents a new measurement mechanism, facilitated by the steady introduction of IPv6 in network nodes and hosts, which exploits native features of the protocol to provide support for performance measurements at the network (IP) layer. IPv6 Extension Headers have been used to carry the triggers involving the measurement activity and the measurement data in-line with the payload data itself, providing a high level of probability that the behaviour of the real user traffic flows is observed. End-to-end one-way delay, jitter, loss, and throughput have been measured for applications operating on top of both reliable and unreliable transports, over different-capacity IPv6 network configurations. We conclude that this technique could form the basis for future Internet measurements that can be dynamically deployed where and when required in a multi-service IP environment

Characterization of Internet Traffic in UUM Wireless Networks

The development in communication technology and the propagation of mobile devices, lightweight, with built-in, high-speed radio access in wireless are making wireless access to the Internet the popular situation rather than a wire line. Whereas, the growth of the wireless network with additional mobile devices in the UUM and increasing number of users led to slow wireless connection. Therefore, understanding the behavior of traffic analysis helps us to develop, manage WLAN technology, and deploy. It help us to apply our workload analysis results to issues in wireless network deployment, such as capacity planning, and potential network optimizations, such as algorithms for load balancing across multiple Access Points (APs) in a wireless network. The trace composes of two parts: firstly, one that connects to the core switch in computer center which is connected with the distribution switches that link the Access Point (APs) with the wireless network at campus, and secondly, another one for the measurement of bulk data transfers and interactive data exchange between two nodes in UUM library, which had been initiated at that time. This thesis investigates the performance network and users' behavior in UUM wireless network

On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels. We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3 adds GitHub lin

TCP smart framing: a segmentation algorithm to reduce TCP latency

TCP Smart Framing, or TCP-SF for short, enables the Fast Retransmit/Recovery algorithms even when the congestion window is small. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP-SF adopts a novel segmentation algorithm: while Classic TCP always tries to send full-sized segments, a TCP-SF source adopts a more flexible segmentation algorithm to try and always have a number of in-flight segments larger than 3 so as to enable Fast Recovery. We motivate this choice by real traffic measurements, which indicate that today's traffic is populated by short-lived flows, whose only means to recover from a packet loss is by triggering a Retransmission Timeout. The key idea of TCP-SF can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server TCP stack only, and can be easily coupled with recent TCP enhancements. The performance of the proposed TCP modification were studied by means of simulations, live measurements and an analytical model. In addition, the analytical model we have devised has a general scope, making it a valid tool for TCP performance evaluation in the small window region. Improvements are remarkable under several buffer management schemes, and maximized by byte-oriented schemes